{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # 导入Pandas\n",
    "import numpy as np # 导入NumPy\n",
    "import matplotlib.pyplot as plt # 导入绘图工具"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.27</td>\n",
       "      <td>-0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.80</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.69</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-0.45</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-0.22</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        X     Y\n",
       "0    0.27 -0.16\n",
       "1   -0.80  0.65\n",
       "2   -0.46 -0.42\n",
       "3   -0.69  0.34\n",
       "4   -0.16 -0.12\n",
       "..    ...   ...\n",
       "195  0.78  0.85\n",
       "196  0.19 -0.11\n",
       "197 -0.34  0.31\n",
       "198 -0.45  0.34\n",
       "199 -0.22  0.21\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('特征构建示意数据集.csv') # 导入数据\n",
    "df # 显示数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Y值')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.X, df.Y) # X,Y散点图\n",
    "plt.xlabel('X值') # x轴\n",
    "plt.ylabel('Y值') # y轴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split # 导入train_test_split\n",
    "df_x = df['X'].to_frame() # 特征集\n",
    "df_y = df['Y'] # 标签集\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_x, df_y, test_size=0.3, random_state=0) # 拆分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lvan/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/base.py:509: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
      "  linalg.lstsq(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression # 导入LinearRegression\n",
    "model = LinearRegression() # 创建模型\n",
    "model.fit(X_train, y_train) # 拟合模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Y值')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.X, df.Y) # 散点图 \n",
    "plt.plot(X_test, model.predict(X_test), linestyle='--', color='red') # 显示拟合曲线\n",
    "plt.xlabel('X值') # x轴\n",
    "plt.ylabel('Y值') # y轴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2_train = X_train.copy() # 新特征训练集\n",
    "X2_test = X_test.copy() # 新特征测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>X2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.0441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.1089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>-0.47</td>\n",
       "      <td>0.2209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>-0.52</td>\n",
       "      <td>0.2704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        X      X2\n",
       "131  0.21  0.0441\n",
       "96   0.33  0.1089\n",
       "181 -0.47  0.2209\n",
       "19   0.13  0.0169\n",
       "153 -0.52  0.2704"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2_train['X2'] = X2_train['X']**2 # 构建新特征X2\n",
    "X2_train.head() # 显示新特征训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2_test['X2'] = X2_test['X']**2 # 构建新特征X2\n",
    "# X2_test # 显示新特征测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = LinearRegression() #创建新模型\n",
    "model2.fit(X2_train, y_train) # 拟合新模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Y值')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.X, df.Y) # 散点图\n",
    "plt.scatter(X_test, model2.predict(X2_test), linestyle='--', color='red') # 新拟合函数曲线\n",
    "plt.xlabel('X值') # x轴\n",
    "plt.ylabel('Y值') # y轴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
